Enhanced Interfacial H2 Activation for Nitrostyrene Catalytic Hydrogenation over Rutile Titania-Supported Gold by Coadsorption: A First-Principles Microkinetic Simulation Study

Perspective on computational reaction prediction using machine learning methods in heterogeneous catalysis

Heterogeneous catalysis plays a significant role in the modern chemical industry. Towards the rational design of novel catalysts, understanding reactions over surfaces is the most essential aspect. Typical industrial catalytic processes such as syngas conversion and methane utilisation can generate a large reaction network comprising thousands of intermediates and reaction pairs. This complexity not only arises from the permutation of transformations between species but also from the extra reaction channels offered by distinct surface sites. Despite the success in investigating surface reactions at the atomic scale, the huge computational expense of ab initio methods hinders the exploration of such complicated reaction networks. With the proliferation of catalysis studies, machine learning as an emerging tool can take advantage of the accumulated reaction data to emulate the output of ab initio methods towards swift reaction prediction. Here, we briefly summarise the conventional workflow of reaction prediction, including reaction network generation, ab initio thermodynamics and microkinetic modelling. An overview of the frequently used regression models in machine learning is presented. As a promising alternative to full ab initio calculations, machine learning interatomic potentials are highlighted. Furthermore, we survey applications assisted by these methods for accelerating reaction prediction, exploring reaction networks, and computational catalyst design. Finally, we envisage future directions in computationally investigating reactions and implementing machine learning algorithms in heterogeneous catalysis.

Gold catalysts containing interstitial carbon atoms boost hydrogenation activity

Supported gold nanoparticles are emerging catalysts for heterogeneous catalytic reactions, including selective hydrogenation. The traditionally used supports such as silica do not favor the heterolytic dissociation of hydrogen on the surface of gold, thus limiting its hydrogenation activity. Here we use gold catalyst particles partially embedded in the pore walls of mesoporous carbon with carbon atoms occupying interstitial sites in the gold lattice. This catalyst allows improved electron transfer from carbon to gold and, when used for the chemoselective hydrogenation of 3-nitrostyrene, gives a three times higher turn-over frequency (TOF) than that for the well-established Au/TiO2 system. The d electron gain of Au is linearly related to the activation entropy and TOF. The catalyst is stable, and can be recycled ten times with negligible loss of both reaction rate and overall conversion. This strategy paves the way for optimizing noble metal catalysts to give an enhanced hydrogenation catalytic performance.

A fast species redistribution approach to accelerate the kinetic Monte Carlo simulation for heterogeneous catalysis

The first-principles kinetic Monte Carlo (kMC) simulation has been demonstrated as a reliable multiscale modeling approach in silico to disclose the interplay among all the elementary steps in a complex reaction network for heterogeneous catalysis. Heterogeneous catalytic systems frequently contain fast surface diffusion processes of some adsorbates while the elementary steps in it would be much slower than those in fast diffusion. Consequently, the kMC simulation for these systems is easily trapped in the sub-basins of a super basin on a potential energy surface due to the continuous and repeated sampling of these fast processes, which would significantly increase the total accessible simulation time and even make it impossible to get the reasonable simulation results using the kMC simulation. In this work, we present an improved fast species redistribution (FSR) method for the kMC simulation to overcome the stiffness problem resulting from the low-barrier surface diffusion to accelerate the heterogeneous catalytic kMC simulation. Taking CO oxidations on Pt(111) and Pt(100) as examples, we demonstrate that the FSR approach can properly reproduce the results of an equivalent first-principles microkinetic model simulation with more reasonable reaction rates. The improved kMC simulation based on the FSR method can accurately incorporate the effect of the fast diffusion of species on the surface and provide several orders of magnitude of acceleration compared to the standard kMC simulation.